Bee-Path: Experiments on Human Mobility

Due to the progress of tracking technologies in smart phones is possible to study empirically human mobility at pedestrian level. Data available for research purposes at this level is scarce and somewhat restricted. ICT... [ view full abstract ]

Due to the progress of tracking technologies in smart phones is possible to study empirically human mobility at pedestrian level. Data available for research purposes at this level is scarce and somewhat restricted. ICT companies as, for instance, mobile phone operators have been traditionally recording the locations of the phone calls, but are reluctant to share the data with the scientific community. Bee-Path's approaches to the challenge of gathering data and therefore better understand human mobility with the use of citizen science strategies. The GPS signal from mobile phones is the best option and we thus went straightforward to obtain the full consent from a large number of individuals (450) that agree to participate in our experiment. First experiment was performed during the weekend of 16th and 17th of June of 2012 and within the Festa de la Ciència i la Tecnologia (Science and Technology Fair) of Barcelona with the support of the Citizen Science Office of the Barcelona Lab. We designed an App under the name Bee-Path and a website where data, codes and results are fully accessible. Volunteers were tracked during the exploration of the stands in the fair. We shared with participants a basic analysis of the data gathered and thus transformed the experiment into a participatory citizen science experience. More than 5,000 valid GPS records were obtained to study 640 flights and 403 stops. In order to reproduce the aggregate statistics we develop a model based on Langevin Dynamics where particles interact with the landscape by means of a dynamic gravitational force field generated by several poles of attractions. The model reproduces with good accuracy collective properties of human in this kind of environments. The approach helps to anticipate spatial distribution of citizens at a neighborhood level and to improve the co-design of public events.